The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for e#cient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information.

Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection.

This research presents our open discovery algorithm which is a text mining algorithm. We demonstrate that this algorithm may be used to uncover information that could form the basis of new hypotheses. In particular, we use it to discover novel uses for Curcumin Longa, a dietary substance, highly regarded for its therapeutic properties in Asia. Several diseases are identified as o#ering novel research contexts for curcumin. We analyze select suggestions: retinal diseases, Crohn's disease and disorders related to the spinal cord. Our analysis suggests that there is strong evidence in favour of a beneficial role for curcumin in these diseases. The evidence is based on curcumin's influence on several genes such as COX-2, TNF-alpha, JNK, p38 MAPK and TGF-beta. This research suggests that our open discovery algorithm may be used to find novel uses for dietary and pharmacologic substances. More generally, open discovery may be used to uncover information that potentially sheds new light on a given topic of interest.

Detailed classifications, controlled vocabularies and organised terminology are widely used in different areas of science and technology. Their relatively recent introduction in molecular biology has been crucial for progress in the analysis of genonics and massive proteomics experiments. Unfortunately the construction of the ontologies, including terminology, classification and entity relations requires considerable effort, including the analysis of massive amounts of literature. We propose here a method that automatically generates classifications of gene-product functions using bibliographic information. The corresponding classification structures mirror the ones constructed by human experts. The analysis of a large structure built for yeast gene-products, and the detailed inspection of various examples, show encouraging properties. In particular, the comparison with the well accepted GO ontology points to different situations in which the automatically derived classification can be useful for assisting human experts in the annotation of ontologies.

Literature mining is the process of extracting and combining facts from scientific publications. In recent years, many studies have resulted in computer programs to extract various molecular biology findings using Medline abstracts or full text articles. This article describes the range of techniques that have been applied in literature mining. In doing so, it divides automated reading into four general subtasks: text categorization, named entity tagging, fact extraction and collection-wide analysis. Special attention is given to the domain particularities of molecular biology.

A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present a computational method that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in the analysis of gene expression data offers an opportunity to incorporate functional information about the genes when dening expression clusters. We have created a method that associates gene expression profiles with known biological functions. Our method has two steps. First, we apply hierarchical clustering to the given gene expression data set. Secondly, we use text from abstracts about genes to (i) resolve hierarchical cluster boundaries to optimize the functional coherence of the clusters and (ii) recognize those clusters that are most functionally coherent. In the case where a gene has not been investigated and therefore lacks primary literature, articles about well-studied homologous genes are added as references. We apply our method to two large gene expression data sets with different properties. The first contains measurements for a subset of wellstudied Saccharomyces cerevisiae genes with multiple literature references, and the second contains newly discovered genes in Drosophila melanogaster; many have no literature references at all. In both cases, we are able to rapidly define and identify the biologically relevant gene expression profiles without manual intervention. In both cases, we identified novel clusters that were not noted by the original investigators.

Gene Expression is the process by which a gene’s coded information is translated into the proteins present and operating in the cell. Changes in gene expression are associated with many important biological phenomena, including morphogenesis and aging, cancer and disease states, and adaptive